On-line state and parameter estimation of an under-actuated underwater vehicle using a modified Dual Unscented Kalman Filter

This paper presents a novel modification of the Dual Unscented Kalman Filter (DUKF) for the on-line concurrent state and parameter estimation. The developed algorithm is successfully applied to an under-actuated underwater vehicle. Like in the case of conventional DUKF the proposed algorithm demonstrates quick convergence of the parameter vector. In addition, experimental results indicate an increased performance when the proposed methodology is utilized. The applicability and performance of the proposed algorithm is experimentally verified by combining the proposed DUKF with a non-linear controller on a modified Videoray ROV in a test tank. The on-line estimation of the vehicle states and dynamic parameters is achieved by fusing data from a Laser Vision System (LVS) and an Inertial Measurement Unit (IMU).

[1]  Markus Vincze,et al.  Multi-rate fusion with vision and inertial sensors , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[2]  Kostas J. Kyriakopoulos,et al.  A visual-servoing scheme for semi-autonomous operation of an underwater robotic vehicle using an IMU and a Laser Vision System , 2010, 2010 IEEE International Conference on Robotics and Automation.

[3]  Andres El-Fakdi,et al.  On the Identification of Non Linear Models of Unmanned Underwater Vehicles , 2003 .

[4]  Kostas J. Kyriakopoulos,et al.  Visual servo control of an underwater vehicle using a Laser Vision System , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[5]  M. Caccia,et al.  Modeling and identification of open-frame variable configuration unmanned underwater vehicles , 2000, IEEE Journal of Oceanic Engineering.

[6]  K.J. Kyriakopoulos,et al.  Target-referenced Localization of an Underwater Vehicle using a Laser-based Vision System , 2006, OCEANS 2006.

[7]  K.J. Kyriakopoulos,et al.  Towards the stabilization of an underactuated underwater vehicle in the presence of unknown disturbances , 2008, OCEANS 2008.

[8]  Louis L. Whitcomb,et al.  Adaptive identification of dynamically positioned underwater robotic vehicles , 2003, IEEE Trans. Control. Syst. Technol..

[9]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[10]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[11]  M. C. VanDyke,et al.  UNSCENTED KALMAN FILTERING FOR SPACECRAFT ATTITUDE STATE AND PARAMETER ESTIMATION , 2004 .

[12]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[13]  W. Marsden.  I and J , 2012 .

[14]  Andres El-Fakdi,et al.  On the identification of non-linear models of unmanned underwater vehicles , 2004 .

[15]  Asgeir J. Sørensen,et al.  Neural network augmented identification of underwater vehicle models , 2004 .